scholarly journals Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa

2021 ◽  
Author(s):  
Davy Uwizera ◽  
Charles Ruranga ◽  
Patrick McSharry
Keyword(s):  
Satellite Imagery ◽  
Low Income ◽  
Google Earth ◽  
The Arctic ◽  
Target Area ◽  
Commercial Building ◽  
Industrial Zone ◽  
Middle Income ◽  
Intelligence Agency ◽  
Spatial Area

<div>In this research we use data from a number of different sources of satellite imagery. Below we describe and visualize various metrics of the datasets being considered. Satellite imagery is retrieved from Google earth which is supported by Data SIO (Scripps Institution of Oceanography), NOAA (National Oceanic and Atmospheric Administration), US. Navy (United States Navy), NGA (National Geospatial-Intelligence Agency), GEBCO (General Bathymetric Chart of the Oceans), Image Landsat, and Image IBCAO (International Bathymetric Chart of the Arctic Ocean). Using random sampling of spatial area in Kigali per target area, 342,843 thousands images were retrieved under the five categories: residential high income (78941), residential low income(162501), residential middle income(101401), commercial building, (67400) and industrial zone,(24400). For the industrial zone, we also included some images from Nairobi, Kenya industrial spatial area. The average number of samples for a category is 86929. The size of the sample per category is proportional to the size of the spatial target area considered per category. Kigali is located at latitude:-1.985070 and longitude:-1.985070, coordinates. Nairobi is located at latitude:-1.286389 and longitude:36.817223, coordinates.</div>

scholarly journals Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa

2021 ◽  
Author(s):  
Davy Uwizera ◽  
Charles Ruranga ◽  
Patrick McSharry
Keyword(s):  
Satellite Imagery ◽  
Low Income ◽  
Google Earth ◽  
The Arctic ◽  
Target Area ◽  
Commercial Building ◽  
Industrial Zone ◽  
Middle Income ◽  
Intelligence Agency ◽  
Spatial Area

<div>In this research we use data from a number of different sources of satellite imagery. Below we describe and visualize various metrics of the datasets being considered. Satellite imagery is retrieved from Google earth which is supported by Data SIO (Scripps Institution of Oceanography), NOAA (National Oceanic and Atmospheric Administration), US. Navy (United States Navy), NGA (National Geospatial-Intelligence Agency), GEBCO (General Bathymetric Chart of the Oceans), Image Landsat, and Image IBCAO (International Bathymetric Chart of the Arctic Ocean). Using random sampling of spatial area in Kigali per target area, 342,843 thousands images were retrieved under the five categories: residential high income (78941), residential low income(162501), residential middle income(101401), commercial building, (67400) and industrial zone,(24400). For the industrial zone, we also included some images from Nairobi, Kenya industrial spatial area. The average number of samples for a category is 86929. The size of the sample per category is proportional to the size of the spatial target area considered per category. Kigali is located at latitude:-1.985070 and longitude:-1.985070, coordinates. Nairobi is located at latitude:-1.286389 and longitude:36.817223, coordinates.</div>

scholarly journals Detecting Demolished Buildings after a Natural Hazard Using High Resolution RGB Satellite Imagery and Modified U-Net Convolutional Neural Networks

2021 ◽  
Vol 13 (11) ◽  
pp. 2176
Author(s):  
Vahid Rashidian ◽  
Laurie G. Baise ◽  
Magaly Koch ◽  
Babak Moaveni
Keyword(s):  
Satellite Imagery ◽  
Natural Hazard ◽  
Google Earth ◽  
Intelligence Agency ◽  
Independent Dataset ◽  
Learning Framework ◽  
And Performance ◽  
Collapsed Buildings ◽  
The U.S ◽  
Trained Network

Collapsed buildings are usually linked with the highest number of human casualties reported after a natural disaster; therefore, quickly finding collapsed buildings can expedite rescue operations and save human lives. Recently, many researchers and agencies have tried to integrate satellite imagery into rapid response. The U.S. Defense Innovation Unit Experimental (DIUx) and National Geospatial Intelligence Agency (NGA) have recently released a ready-to-use dataset known as xView that contains thousands of labeled VHR RGB satellite imagery scenes with 30-cm spatial and 8-bit radiometric resolutions, respectively. Two of the labeled classes represent demolished buildings with 1067 instances and intact buildings with more than 300,000 instances, and both classes are associated with building footprints. In this study, we are using the xView imagery, with building labels (demolished and intact) to create a deep learning framework for classifying buildings as demolished or intact after a natural hazard event. We have used a modified U-Net style fully convolutional neural network (CNN). The results show that the proposed framework has 78% and 95% sensitivity in detecting the demolished and intact buildings, respectively, within the xView dataset. We have also tested the transferability and performance of the trained network on an independent dataset from the 19 September 2017 M 7.1 Pueblo earthquake in central Mexico using Google Earth imagery. To this end, we tested the network on 97 buildings including 10 demolished ones by feeding imagery and building footprints into the trained algorithm. The sensitivity for intact and demolished buildings was 89% and 60%, respectively.

Practicing Anthropology in a Community Health Center

1981 ◽  
Vol 3 (3) ◽  
pp. 9-40 ◽  
Author(s):  
Janice Hogle
Keyword(s):  
Health Center ◽  
Low Income ◽  
Community Health Center ◽  
Target Area ◽  
Underserved Area ◽  
Middle Income ◽  
White Families ◽  
Housing Projects ◽  
Medically Underserved Area ◽  
The One

Our association with a community based effort to start a neighborhood health center represents one of the Medical Anthropology Program's longer running involvements in urban Hartford. The "community" with which we have been affiliated is now the target area of the one and a half year old Health Center. Two contiguous public housing projects, containing around 8,000 people, make up the "community." The larger of the projects is low-income and federally owned, while the smaller one is middle-income and state owned. Within the past decade, Puerto Ricans have become the dominant (60%+) ethnic group in the projects, followed by blacks (30%+) and a few white families. The community is geographically, culturally, linguistically and medically isolated from the mainstream of Hartford health services. It has been designated a Medically Underserved Area (MUA), and in 1978, a Health Manpower Shortage Area (HMSA), making it eligible for assistance from the National Health Service Corps.

scholarly journals Experiences and impact of international medical volunteering: a multi-country mixed methods study

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e041599 ◽  
Author(s):  
Mary McCauley ◽  
Joanna Raven ◽  
Nynke van den Broek
Keyword(s):  
Low Income ◽  
Online Survey ◽  
Positive Impact ◽  
Healthcare Providers ◽  
International Medical ◽  
Middle Income ◽  
Use Of Resources ◽  
The Uk ◽  
Medical Volunteers ◽  
The Impact

ObjectiveTo assess the experience and impact of medical volunteers who facilitated training workshops for healthcare providers in maternal and newborn emergency care in 13 countries.SettingsBangladesh, Ghana, India, Kenya, Malawi, Namibia, Nigeria, Pakistan, Sierra Leone, South Africa, Tanzania, UK and Zimbabwe.ParticipantsMedical volunteers from the UK (n=162) and from low-income and middle-income countries (LMIC) (n=138).Outcome measuresExpectations, experience, views, personal and professional impact of the experience of volunteering on medical volunteers based in the UK and in LMIC.ResultsUK-based medical volunteers (n=38) were interviewed using focus group discussions (n=12) and key informant interviews (n=26). 262 volunteers (UK-based n=124 (47.3%), and LMIC-based n=138 (52.7%)) responded to the online survey (62% response rate), covering 506 volunteering episodes. UK-based medical volunteers were motivated by altruism, and perceived volunteering as a valuable opportunity to develop their skills in leadership, teaching and communication, skills reported to be transferable to their home workplace. Medical volunteers based in the UK and in LMIC (n=244) reported increased confidence (98%, n=239); improved teamwork (95%, n=232); strengthened leadership skills (90%, n=220); and reported that volunteering had a positive impact for the host country (96%, n=234) and healthcare providers trained (99%, n=241); formed sustainable partnerships (97%, n=237); promoted multidisciplinary team working (98%, n=239); and was a good use of resources (98%, n=239). Medical volunteers based in LMIC reported higher satisfaction scores than those from the UK with regards to impact on personal and professional development.ConclusionHealthcare providers from the UK and LMIC are highly motivated to volunteer to increase local healthcare providers’ knowledge and skills in low-resource settings. Further research is necessary to understand the experiences of local partners and communities regarding how the impact of international medical volunteering can be mutually beneficial and sustainable with measurable outcomes.

scholarly journals Socioeconomic Disparities in Foods/Beverages and Nutrients Consumed by U.S. Adolescents When Snacking: National Health and Nutrition Examination Survey 2005–2018

Nutrients ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 2530
Author(s):  
Navika Gangrade ◽  
Janet Figueroa ◽  
Tashara M. Leak
Keyword(s):  
National Health ◽  
Low Socioeconomic Status ◽  
Low Income ◽  
High Income ◽  
Future Research ◽  
Nutrition Examination Survey ◽  
Middle Income ◽  
Low Socioeconomic ◽  
Health And Nutrition ◽  
Income Ratio

Snacking contributes a significant portion of adolescents’ daily energy intake and is associated with poor overall diet and increased body mass index. Adolescents from low socioeconomic status (SES) households have poorer snacking behaviors than their higher-SES counterparts. However, it is unclear if the types of food/beverages and nutrients consumed during snacking differ by SES among adolescents. Therefore, this study examines SES disparities in the aforementioned snacking characteristics by analyzing the data of 7132 adolescents (12–19 years) from the National Health and Nutrition Examination Survey 2005–2018. Results reveal that adolescents from low-income households (poverty-to-income ratio (PIR) ≤ 1.3) have lower odds of consuming the food/beverage categories “Milk and Dairy” (aOR: 0.74; 95% CI: 0.58-0.95; p = 0.007) and “Fruits” (aOR: 0.62, 95% CI: 0.50–0.78; p = 0.001) as snacks and higher odds of consuming “Beverages” (aOR: 1.45; 95% CI: 1.19-1.76; p = 0.001) compared to those from high-income households (PIR > 3.5). Additionally, adolescents from low- and middle-income (PIR > 1.3–3.5) households consume more added sugar (7.98 and 7.78 g vs. 6.66 g; p = 0.012, p = 0.026) and less fiber (0.78 and 0.77 g vs. 0.84 g; p = 0.044, p = 0.019) from snacks compared to their high-income counterparts. Future research is necessary to understand factors that influence snacking among adolescents, and interventions are needed, especially for adolescents from low-SES communities.

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